Dimensionality hyper-reduction and machine learning for dynamical systems with varying parameters

Thesis (Ph.D.)--University of Washington, 2016-08

Machine learning and data decompositions for complex networked dynamical systems

Thesis (Ph.D.)--University of Washington, 2016-08

Modelo predictivo de radiación solar mediante técnicas de machine learning: aplicación a la isla de Gran Canaria

Programa de doctorado: Tecnología Industrial. La fecha de publicación es la fecha de lectura.

Essays on Machine Learning and Hedonic Models

Thesis (Ph.D.)--University of Washington, 2016-08

A heuristic algorithm for the two-machine re-entrant shop scheduling

Abstract not available

Heuristics for the two-stage job shop scheduling problem with a bottleneck machine

Abstract not available

Earliness penalties on a single machine subject to precedence constraints: SLK due date assignment

Abstract not available

A polynomial algorithm for the three-machine open shop with a bottleneck machine

Abstract not available

Ghost Machine: a Sound Performance

Ghost Machine is an encounter between a person and a machine in a suburban shed. The machine reveals the story of a man haunted by an image that seems to shift and change as it sits on the wall of his study. In an attempt to locate the origins of the image he builds a viewing machine to finally confront it. Ghost Machine is based on The Mezzotint (1904) by M.R James, retold as a suburban ghost story. It was part of SENSE at Mayfest in Bristol between 24th - 26th May 2013.

Two-stage open shop scheduling with a bottleneck machine

Abstract not available

Two-machine open shop scheduling with special transportation times

Abstract not available

Flow shop scheduling problems under machine-dependent precedence constraints

Abstract not available

The stochastic geometric machine model

This paper introduces the stochastic version of the Geometric Machine Model for the modelling of sequential, alternative, parallel (synchronous) and nondeterministic computations with stochastic numbers stored in a (possibly infinite) shared memory. The programming language L(D! 1), induced by the Coherence Space of Processes D! 1, can be applied to sequential and parallel products in order to provide recursive definitions for such processes, together with a domain-theoretic semantics of the Stochastic Arithmetic. We analyze both the spacial (ordinal) recursion, related to spacial modelling of the stochastic memory, and the temporal (structural) recursion, given by the inclusion relation modelling partial objects in the ordered structure of process construction.

The MUSE Machine -- an Architecture for Structured Data Flow Computation

Computers employing some degree of data flow organisation are now well established as providing a possible vehicle for concurrent computation. Although data-driven computation frees the architecture from the constraints of the single program counter, processor and global memory, inherent in the classic von Neumann computer, there can still be problems with the unconstrained generation of fresh result tokens if a pure data flow approach is adopted. The advantages of allowing serial processing for those parts of a program which are inherently serial, and of permitting a demand-driven, as well as data-driven, mode of operation are identified and described. The MUSE machine described here is a structured architecture supporting both serial and parallel processing which allows the abstract structure of a program to be mapped onto the machine in a logical way.